A new fast local laplacian completed local ternary count (FLL-CLTC) for facial image classification

Alalayah, Khaled M. and Reyazur Rashid, Irshad and Rassem, Taha H. and Mohammed, Badiea Abdulkarem (2020) A new fast local laplacian completed local ternary count (FLL-CLTC) for facial image classification. IEEE Access, 8. 98244 - 98254. ISSN 2169-3536. (Published)

[img] Pdf
Restricted to Repository staff only

Download (1MB) | Request a copy
A New Fast Local Laplacian Completed Local Ternary Count.pdf

Download (198kB) | Preview


Face recognition is one of the most interesting areas of research areas because of its importance in authentication and security. Differentiating between different facial images is not easy because of the similarities in facial features. Human faces can also be covered obscured by eyeglasses, facial expressions and hairstyles can also be changed causing difficulty in finding similar faces. Thus, the need for powerful image features has become a critical issue in the face recognition systems. Many texture features have been used in these systems, including Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC) and Completed Local Ternary Pattern (CLTP). In this paper, a new texture descriptor, namely, Completed Local Ternary Count (CLTC), is proposed by adding a threshold value for the CLBC to overcome its sensitivity to noise drawback. The CLTC is also enhanced by adding the Fast-Local Laplacian filter during the pre-processing stage to increase the discriminative property of the proposed descriptor. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) texture descriptor is evaluated for face recognition task using five different face image datasets. The experimental results of the FLL-CLTC showed that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors in term of recognition accuracy. The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively.

Item Type: Article
Additional Information: Indexed by Scopus & WOS
Uncontrolled Keywords: Face recognition; Texture descriptor; Local binary pattern; Local binary coun;, Fast local laplacian
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Dr. Taha Hussein Alaaldeen Rassem
Date Deposited: 14 Jul 2020 02:51
Last Modified: 14 Jul 2020 02:51
URI: http://umpir.ump.edu.my/id/eprint/28457
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item